Kính hiển vi trình duyệt cho phép tính của mô hình.

CMET makes the model and its measurements part of the same browser experience. Generation and supported analysis run locally through ONNX Runtime, with WebGPU preferred and WASM available as an explicit fallback rather than a hidden remote service.

That makes the runtime boundary inspectable: model graphs, tokenizers, manifests and frozen evidence are same-origin static artifacts with pinned identities.

The system studies a specific next-token decision at a specific sequence position. The interface can show the generated token, probability trajectory, attention paths and writes that moved the decision, rather than presenting a large explanation detached from one measured forward pass.

The research workspace then renders frozen evidence such as logit-lens views, interventions, path tests and annotations as receipts with their own provenance.

CMET separates direct observations, estimates, intervention-supported results, path or interaction evidence and replicated mechanisms. A fluent sentence describing a result does not change its evidence class.

This is a useful discipline for any scientific interface: the visual story should make uncertainty legible instead of allowing labels to outrun the run that produced them.

The current platform is a scoped instrument for small decoder-only models and declared tasks. Calibration, controls, cross-prompt replication and wider causal qualification remain separate obligations whenever a result moves from exploration toward a research claim.

The product is strongest when it shows the measured trace and its limits together.